Modelling process uncertainty:
Three examples

Example: Real-time forecasts during the 2013-16 Ebola epidemic

The unknown

• Community/hospital/funeral transmission
• Spatial dynamics
• Changes in behaviour
• Changes in reporting
• Interventions
• Seasonality
• etc

The known

• Average incubation period (~9 days)
• Average infectious period (~11 days)
• Case-fatality rate (~70%)

WHO Ebola response team (2014)

Transmission intensity as a stochastic process

\(d\log(R_0(t)) = \sigma dW_t\)

Dureau (2013)

Particle MCMC

• Method for filtering trajectories consistent with data
• Highly parallelisable

Andrieu (2010), Murray (2013)

Example: Ebola outbreak in Lofa Country, Liberia

Filtered trajectories tell us something about dynamics

An attempt to tease out factors that controlled Ebola

An attempt to tease out factors that controlled Ebola

Example: Age of infection in childhood infections

References

Assessing the performance of real-time epidemic forecasts.
S. Funk, A. Camacho, A. J. Kucharski, R. Lowe, R. M. Eggo, W. J. Edmunds.
bioRxiv (2018) doi:10.1101/177451

Real-time forecasting of infectious disease dynamics with a stochastic semi-mechanistic model.
S. Funk, A. Camacho, A. J. Kucharski, R. M. Eggo, W. J. Edmunds.
Epidemics (2018) 22:56–61.

The impact of control strategies and behavioural changes on the elimination of Ebola from Lofa County, Liberia.
S. Funk, I. Ciglenecki, A. Tiffany, E. Gignoux et al.
Philos T R Soc B (2017) 372:1721.

Temporal Changes in Ebola Transmission in Sierra Leone and Implications for Control Requirements: a Real-time Modelling Study.
A. Camacho, A. Kucharski, Y. Aki-Sawyerr, S. Funk et al.
PLoS Curr (2015) Feb 10. Edition 1